This episode explores the foundations and current methods of deep learning applied to natural language processing (NLP). Against the backdrop of the increasing popularity of large language models like ChatGPT, the speaker delves into the Word2vec algorithm, a decade-old yet highly successful method for learning vector representations of words. More significantly, the lecture explains how Word2vec leverages distributional semantics—representing word meaning by its context—to create dense vectors capturing word similarity. For instance, the algorithm calculates the probability of word co-occurrence within a defined window, optimizing word vectors to maximize these probabilities. The speaker then explains the mathematical underpinnings, including gradient descent and the softmax function, used to refine these vectors. This detailed explanation of Word2vec serves as a foundational step towards understanding more complex NLP models and their applications. What this means for the field is a deeper understanding of how to represent and compute with word meaning, paving the way for more sophisticated NLP systems.
